Geo-based statistical models for vulnerability prediction of highway network segments

Keren Pollak, Ammatzia Peled, Shalom Hakkert

Research output: Contribution to journalArticlepeer-review


This study describes four statistical models Poisson; Negative Binomial; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial which were devised in order to examine traffic accidents and estimate the best probability estimating model in terms of future risk assessment at interurban road sections. The study was conducted on four sets of fixed-length sections of the road network: 500, 750, 1000, and 1500 m. The contribution of transportation and spatial parameters as predictors of road accident rates was evaluated for all four data sets separately. In addition, the Empirical Bayes method was applied. This method uses historical accidents information, allowing regression to the mean phenomenon so as to improve model results. The study was performed using Geographic Information System (GIS) software. Other analyses, such as statistical analyses combined with spatial parameters, interactions, and examination of other geographical areas, were also performed. The results showed that the short road sections data sets of 500 and 750 m yielded the most stable models. This allows focused treatment on short sections of the road network as a way to save resources (enforcement; education and information; finance) and potentially gain maximum benefit at minimum investment. It was found that the significant parameters affecting accident rates are: curvature of the road section; the region and traffic volume. An interaction between the region and traffic volume was also found.

Original languageEnglish
Pages (from-to)619-637
Number of pages19
JournalISPRS International Journal of Geo-Information
Issue number2
StatePublished - Jun 2014

Bibliographical note

Publisher Copyright:
© 2014 by the authors; licensee MDPI, Basel, Switzerland.


  • Empirical Bayes
  • GIS
  • Highway
  • Negative binomial
  • Poisson
  • Probability models
  • Spatial
  • Traffic accidents
  • Transportation
  • Zero-Inflated

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)


Dive into the research topics of 'Geo-based statistical models for vulnerability prediction of highway network segments'. Together they form a unique fingerprint.

Cite this